Identification of Glucose-Binding Pockets in Human Serum Albumin Using Support Vector Machine and Molecular Dynamics Simulations

Glycopolymeric Nanoparticles Enrich Less Immunogenic Protein Coronas, Reduce Mononuclear Phagocyte Clearance, and Improve Tumor Delivery Compared to PEGylated Nanoparticles

Nanoparticles (NPs) offer significant promise as drug delivery vehicles; however, their in vivo efficacy is often hindered by the formation of a protein corona (PC), which influences key physiological responses such as blood circulation time, biodistribution, cellular uptake, and intracellular localization. Understanding NP-PC interactions is crucial for optimizing NP design for biomedical applications. Traditional approaches have utilized hydrophilic polymer coatings like polyethylene glycol (PEG) to resist protein adsorption, but glycopolymer-coated nanoparticles have emerged as potential alternatives due to their biocompatibility and ability to reduce the adsorption of highly immunogenic proteins. In this study, we synthesized and characterized glycopolymer-based poly[2-(diisopropylamino)ethyl methacrylate-b-poly(methacrylamidoglucopyranose) (PDPA-b-PMAG) NPs as an alternative to PEGylated NPs. We characterized the polymers using a range of techniques to establish their molecular weight and chemical composition. PMAG and PEG-based NPs showed equivalent physicochemical properties with sizes of ∼100 nm, spherical morphology, and neutral surface charges. We next assessed the magnitude of protein adsorption on both NPs and catalogued the identity of the adsorbed proteins using mass spectrometry-based techniques. The PMAG NPs were found to adsorb fewer proteins in vitro as well as fewer immunogenic proteins such as Immunoglobulins and Complement proteins. Flow cytometry and confocal microscopy were employed to examine cellular uptake in RAW 264.7 macrophages and MDA-MB-231 tumor cells, where PMAG NPs showed higher uptake into tumor cells over macrophages. In vivo studies in BALB/c mice with orthotopic 4T1 breast cancer xenografts showed that PMAG NPs exhibited prolonged circulation times and enhanced tumor accumulation compared to PEGylated NPs. The biodistribution analysis also revealed greater selectivity for tumor tissue over the liver for PMAG NPs. These findings highlight the potential of glycopolymeric NPs to improve tumor targeting and reduce macrophage uptake compared to PEGylated NPs, offering significant advancements in cancer nanomedicine and immunotherapy.

A review on advancements in feature selection and feature extraction for high-dimensional NGS data analysis

Recent advancements in biomedical technologies and the proliferation of high-dimensional Next Generation Sequencing (NGS) datasets have led to significant growth in the bulk and density of data. The NGS high-dimensional data, characterized by a large number of genomics, transcriptomics, proteomics, and metagenomics features relative to the number of biological samples, presents significant challenges for reducing feature dimensionality. The high dimensionality of NGS data poses significant challenges for data analysis, including increased computational burden, potential overfitting, and difficulty in interpreting results. Feature selection and feature extraction are two pivotal techniques employed to address these challenges by reducing the dimensionality of the data, thereby enhancing model performance, interpretability, and computational efficiency. Feature selection and feature extraction can be categorized into statistical and machine learning methods. The present study conducts a comprehensive and comparative review of various statistical, machine learning, and deep learning-based feature selection and extraction techniques specifically tailored for NGS and microarray data interpretation of humankind. A thorough literature search was performed to gather information on these techniques, focusing on array-based and NGS data analysis. Various techniques, including deep learning architectures, machine learning algorithms, and statistical methods, have been explored for microarray, bulk RNA-Seq, and single-cell, single-cell RNA-Seq (scRNA-Seq) technology-based datasets surveyed here. The study provides an overview of these techniques, highlighting their applications, advantages, and limitations in the context of high-dimensional NGS data. This review provides better insights for readers to apply feature selection and feature extraction techniques to enhance the performance of predictive models, uncover underlying biological patterns, and gain deeper insights into massive and complex NGS and microarray data.

Induction Motor Fault Classification Based on Combined Genetic Algorithm with Symmetrical Uncertainty Method for Feature Selection Task

This research proposes a method to improve the capability of a genetic algorithm (GA) to choose the best feature subset by incorporating symmetrical uncertainty (SU) to rank the features and remove redundant features. The proposed method is a combination of symmetrical uncertainty and a genetic algorithm (SU-GA). In this study, feature selection is implemented on four different conditions of an induction motor: normal, broken bearings, a broken rotor bar, and a stator winding short circuit. The Hilbert-Huang transform (HHT) is then used to analyze the current signal in these four motor conditions. After that, the feature selection is used to find the best feature subset for the classification task. A support vector machine (SVM) was used for the feature classification. Three feature selection methods were implemented: SU, GA, and SU-GA. The results show that SU-GA obtained better accuracy with fewer selected features. In addition, to simulate and analyze the actual operating situation of the induction motors, three different magnitudes of white noise were added with the following signal-to-noise ratios (SNR): 40 dB, 30 dB, and 20 dB. Finally, the results show that the proposed method has a better classification capability.

Characterization of glucose-binding proteins isolated from health volunteers and human type 2 diabetes mellitus patients

Glucose is one of the most important monosaccharides. Although hyperglycemia in type 2 diabetes mellitus (T2DM) lead to a series of changes; however, little is known about the alterations of serum proteins in T2DM, especially those proteins with glucose affinity. In this study, the glucose-binding proteins (GlcBPs) of serum were isolated from 30 health volunteer (HV) and 30 T2DM patients by glucose-magnetic particle conjugates (GMPC) and identified by mass spectrum analysis. Gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) indicated the main gene annotations and pathways of this GlcBPs, while Motif-X webtool provided the potential glucose-binding domains. Further docking analysis and glycan microarray were used to understand the interaction between the glucose and glucose-binding domains. A total of 149 and 119 GlcBPs were identified from HV and T2DM cases. Four hundred and sixty-eight GO annotations in 165 identified GlcBPs were available, while the majority involved in cellular processes and binding function. A short peptide, EGDEEITCLNGFWLE, which was derived from the Motif-X analysis, presented a high-binding ability to the glucose from both docking analysis and glycan analysis. GMPC provides a powerful tool for GlcBPs isolation and indicates the alteration of GlcBPs in T2DM.

Cannabis and Cannabinoids in Reproduction and Fertility: Where We Stand

Although cannabis use is increasing in general population, their prevalence among young adults is remarkably high. In recent years, their medical use gained a renewed interest. However, it can underline the reputation of cannabis being a harmless drug. Between cannabinoids, uniquely found on the cannabis plant, Δ⁹-tetrahydrocannabinol (THC) is the well-studied compound. It is responsible for the psychoactive effects via central cannabinoid receptors. Nevertheless, cannabinoids interact with other chemical signalling systems such as the hypothalamic-pituitary-gonadal axis. THC indirectly decreases gonadotropin-releasing hormone (GnRH) secretion by the hypothalamus. The consequences are diverse, and several key hormones are affected. THC disturbs important reproductive events like folliculogenesis, ovulation and sperm maturation and function. Although generally accepted that cannabinoid consumption impacts male and female fertility, prevailing evidence remains largely on pre-clinical studies. Here, we introduce cannabinoids and the endocannabinoid system, and we review the most prominent clinical evidence about cannabis consumption in reproductive potential and teratogenicity.

Surface roughness influences the protein corona formation of glycosylated nanoparticles and alter their cellular uptake

Recently the role of protein absorption in nanoparticle drug delivery has gathered significant attention as the protein corona can significantly decide on the fate of nanoparticles in the body. Although it is known that the surface chemistry will significantly influence the amount and type of bound protein, there is little known about the effect of surface roughness and surface topography on the interaction. In this work, we show how patchy nanoparticles can noticeably reduce the adsorption of proteins compared to spherical nanoparticles with a smooth surface as demonstrated using six ABC triblock terpolymers based on glucose, mannose and galactose. To obtain patchy nanoparticles, poly(2-d-sugar ethyl acrylate)-b-poly (n-butyl acrylate)-b-poly(4-vinyl pyridine) (PSugEA-b-PBuA-b-P4VP) was prepared by reversible addition-fragmentation chain-transfer (RAFT) polymerization and assembled into nanoparticles with a patch-like appearance and a hydrodynamic diameter of around 130-160 nm. As control, smooth nanoparticles were prepared from poly(2-d-sugar ethyl acrylate)-b-poly (n-butyl acrylate)-b-polystyrene (PSugEA-b-PBuA-b-PS). The patchy nanoparticles displayed significantly reduced protein absorption when exposed to serum-supplemented cell culture media, as observed using dynamic light scattering. The smooth particles, however, supported the formation of a large protein corona. Additionally, an enrichment of haemoglobin was observed in the corona compared to the serum protein in solution. The amount of albumin on the surface was observed to be dependent on the type of sugar with glucose resulting in the highest absorption. The protein corona led to cellular uptake that was unrelated to the underlying sugar, which was supposed to help targeting specific cell lines. This example demonstrated how the protein corona can override any attempts to target receptor expressing cells.

Streptozocin; a GLUT2 binding drug, interacts with human serum albumin at loci h6DOM3-h7DOM3

Streptozocin (STZ) is a broad range antibiotic, highly genotoxic, antineoplastic and hyperglycemic. HSA is the most abundant protein in physiology and it binds to almost all exogenic and endogenic ligands, including drugs. STZ-induced fluorescence quenching of HSA has been done at pH 7.4, pH 3.5 and at pH 7.4 with 4.5 M urea at temperatures 286 K, 291 K, and 306 K. K_sv found to be 10³ M^-1, binding constant 1.5X10³M^-1 and binding sites ~1. But, K_sv for HSA and glucopyranose interaction was found lesser than that of HSA-STZ binding. Binding of STZ/glucopyranose on HSA seems to result in complex formation as calculated K_q > 10¹⁰ M^-1 s^-1. The number of binding sites, binding constants, and binding energies were increased with temperature. The ΔG⁰, ΔH⁰, and ΔS⁰ for HSA-STZ interaction were found to be -17.7 × 10³ J·mol^-1; 2.34 × 10⁵ J·mol^-1 and 841 JK^-1 mol^-1 respectively at pH 7.4 and 291 K. The comparative bindings of N, F and I states of HSA with STZ and their molecular docking analyses indicate that IIIA-B junction (i.e., inter-helix h6_DOM3-h7_DOM3) is the probable binding site, a locus close to fatty acid binding site-5. These results could be useful for therapeutic and analytical exploitation of STZ, as albumin used as the vehicle for drug delivery.

Feature selection methods for big data bioinformatics: A survey from the search perspective

This paper surveys main principles of feature selection and their recent applications in big data bioinformatics. Instead of the commonly used categorization into filter, wrapper, and embedded approaches to feature selection, we formulate feature selection as a combinatorial optimization or search problem and categorize feature selection methods into exhaustive search, heuristic search, and hybrid methods, where heuristic search methods may further be categorized into those with or without data-distilled feature ranking measures.